基于隐空间拼接与对比学习的语音匿名方法研究OA

With the widespread application of speech technology,voice privacy protection faces severe challenges.A speech anonymization method(kLCAnoy++)based on latent space splicing and contrastive learning was proposed,aiming to effectively remove speaker identity information while preserving speech content intelligibility and naturalness.The method first employed the pretrained WavLM model to extract self-supervised speech features.Subsequently,it matched target features in a diverse reference audio feature library using the k-nearest neighbors algorithm,achieving latent space feature splicing to generate preliminary anonymized speech.Finally,a contrastive learning mechanism was introduced,treating natural splicing as positive samples and k-nearest neighbor splicing as negative samples,to optimize the vocoder decoding process and enhance the coherence and naturalness of anonymized speech.Experiments on the VCTK dataset demonstrated that the proposed method significantly outperformed baseline approaches,with visualization analysis further confirming its effectiveness in disentangling speaker identity information.